Overview

Dataset statistics

Number of variables15
Number of observations192
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)1.0%
Total size in memory22.6 KiB
Average record size in memory120.7 B

Variable types

Categorical5
Numeric9
Boolean1

Alerts

Me3 has constant value ""Constant
Dataset has 2 (1.0%) duplicate rowsDuplicates
Compound is highly overall correlated with Me1 and 2 other fieldsHigh correlation
Ideal_Bond_Length_Sum is highly overall correlated with Mendeleev_Number and 1 other fieldsHigh correlation
Martynov_Batsanov_Electronegativity is highly overall correlated with Valence_Electron_NumberHigh correlation
Me1 is highly overall correlated with Compound and 1 other fieldsHigh correlation
Me2 is highly overall correlated with Compound and 2 other fieldsHigh correlation
Mendeleev_Number is highly overall correlated with Ideal_Bond_Length_Sum and 1 other fieldsHigh correlation
PT-content is highly overall correlated with Tolerance_Factor and 1 other fieldsHigh correlation
Tolerance_Factor is highly overall correlated with PT-content and 1 other fieldsHigh correlation
Valence_Electron_Number is highly overall correlated with Ideal_Bond_Length_Sum and 1 other fieldsHigh correlation
frac-Me1 is highly overall correlated with frac-Me2High correlation
frac-Me2 is highly overall correlated with frac-Me1 and 1 other fieldsHigh correlation
frac-Me3 is highly overall correlated with Compound and 3 other fieldsHigh correlation
x(BiMe1Me2)O3 is highly overall correlated with PT-content and 1 other fieldsHigh correlation
frac-Me3 is highly imbalanced (62.3%)Imbalance
x(BiMe1Me2)O3 has 2 (1.0%) zerosZeros
PT-content has 2 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-05 12:29:00.193837
Analysis finished2023-12-05 12:29:05.829754
Duration5.64 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Compound
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
BiScIn
26 
Bi(NiTi)
15 
BF
14 
Bi(GaSc)
14 
Bi(ZnW)
 
9
Other values (19)
114 

Length

Max length10
Median length8
Mean length6.375
Min length2

Characters and Unicode

Total characters1224
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowBi(GaSc)
2nd rowBi(GaSc)
3rd rowBi(GaSc)
4th rowBi(GaSc)
5th rowBi(GaSc)

Common Values

ValueCountFrequency (%)
BiScIn 26
 
13.5%
Bi(NiTi) 15
 
7.8%
BF 14
 
7.3%
Bi(GaSc) 14
 
7.3%
Bi(ZnW) 9
 
4.7%
BiSc 9
 
4.7%
BiZnTi 9
 
4.7%
Bi(MgW) 9
 
4.7%
BiMn 8
 
4.2%
Bi(ScMgTi) 8
 
4.2%
Other values (14) 71
37.0%

Length

2023-12-05T20:29:05.888139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
biscin 26
 
13.5%
bi(niti 15
 
7.8%
bf 14
 
7.3%
bi(gasc 14
 
7.3%
bi(znw 9
 
4.7%
bisc 9
 
4.7%
biznti 9
 
4.7%
bi(mgw 9
 
4.7%
bimn 8
 
4.2%
bi(scmgti 8
 
4.2%
Other values (14) 71
37.0%

Most occurring characters

ValueCountFrequency (%)
i 243
19.9%
B 192
15.7%
( 97
 
7.9%
) 97
 
7.9%
n 76
 
6.2%
S 70
 
5.7%
c 70
 
5.7%
Z 46
 
3.8%
T 44
 
3.6%
M 43
 
3.5%
Other values (15) 246
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 538
44.0%
Lowercase Letter 492
40.2%
Open Punctuation 97
 
7.9%
Close Punctuation 97
 
7.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 192
35.7%
S 70
 
13.0%
Z 46
 
8.6%
T 44
 
8.2%
M 43
 
8.0%
N 39
 
7.2%
I 33
 
6.1%
F 23
 
4.3%
G 19
 
3.5%
W 18
 
3.3%
Other values (3) 11
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
i 243
49.4%
n 76
 
15.4%
c 70
 
14.2%
g 35
 
7.1%
b 25
 
5.1%
a 19
 
3.9%
r 11
 
2.2%
e 9
 
1.8%
u 2
 
0.4%
o 2
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1030
84.2%
Common 194
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 243
23.6%
B 192
18.6%
n 76
 
7.4%
S 70
 
6.8%
c 70
 
6.8%
Z 46
 
4.5%
T 44
 
4.3%
M 43
 
4.2%
N 39
 
3.8%
g 35
 
3.4%
Other values (13) 172
16.7%
Common
ValueCountFrequency (%)
( 97
50.0%
) 97
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 243
19.9%
B 192
15.7%
( 97
 
7.9%
) 97
 
7.9%
n 76
 
6.2%
S 70
 
5.7%
c 70
 
5.7%
Z 46
 
3.8%
T 44
 
3.6%
M 43
 
3.5%
Other values (15) 246
20.1%

x(BiMe1Me2)O3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41224479
Minimum0
Maximum1
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:05.991679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.27875
median0.4
Q30.54
95-th percentile0.8
Maximum1
Range1
Interquartile range (IQR)0.26125

Descriptive statistics

Standard deviation0.2104026
Coefficient of variation (CV)0.51038267
Kurtosis-0.16504904
Mean0.41224479
Median Absolute Deviation (MAD)0.135
Skewness0.37400405
Sum79.151
Variance0.044269254
MonotonicityNot monotonic
2023-12-05T20:29:06.093692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.5 20
 
10.4%
0.4 18
 
9.4%
0.3 18
 
9.4%
0.2 17
 
8.9%
0.1 15
 
7.8%
0.6 11
 
5.7%
0.35 8
 
4.2%
0.7 7
 
3.6%
0.8 6
 
3.1%
0.15 5
 
2.6%
Other values (36) 67
34.9%
ValueCountFrequency (%)
0 2
 
1.0%
0.05 3
 
1.6%
0.1 15
7.8%
0.15 5
 
2.6%
0.2 17
8.9%
0.22 1
 
0.5%
0.25 4
 
2.1%
0.2735 1
 
0.5%
0.2805 1
 
0.5%
0.3 18
9.4%
ValueCountFrequency (%)
1 2
 
1.0%
0.9 4
2.1%
0.8 6
3.1%
0.75 1
 
0.5%
0.73 1
 
0.5%
0.72 1
 
0.5%
0.71 1
 
0.5%
0.7 7
3.6%
0.69 1
 
0.5%
0.65 2
 
1.0%

Me1
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Sc
60 
Zn
30 
Mg
26 
Ni
21 
Ga
15 
Other values (6)
40 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters384
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGa
2nd rowGa
3rd rowGa
4th rowGa
5th rowGa

Common Values

ValueCountFrequency (%)
Sc 60
31.2%
Zn 30
15.6%
Mg 26
13.5%
Ni 21
 
10.9%
Ga 15
 
7.8%
Fe 14
 
7.3%
Mn 8
 
4.2%
Yb 7
 
3.6%
In 7
 
3.6%
Lu 2
 
1.0%

Length

2023-12-05T20:29:06.183341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sc 60
31.2%
zn 30
15.6%
mg 26
13.5%
ni 21
 
10.9%
ga 15
 
7.8%
fe 14
 
7.3%
mn 8
 
4.2%
yb 7
 
3.6%
in 7
 
3.6%
lu 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
S 60
15.6%
c 60
15.6%
n 45
11.7%
M 34
8.9%
Z 30
7.8%
g 26
6.8%
N 21
 
5.5%
i 21
 
5.5%
a 15
 
3.9%
G 15
 
3.9%
Other values (9) 57
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 192
50.0%
Lowercase Letter 192
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 60
31.2%
M 34
17.7%
Z 30
15.6%
N 21
 
10.9%
G 15
 
7.8%
F 14
 
7.3%
Y 7
 
3.6%
I 7
 
3.6%
L 2
 
1.0%
C 2
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
c 60
31.2%
n 45
23.4%
g 26
13.5%
i 21
 
10.9%
a 15
 
7.8%
e 14
 
7.3%
b 7
 
3.6%
u 2
 
1.0%
o 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 60
15.6%
c 60
15.6%
n 45
11.7%
M 34
8.9%
Z 30
7.8%
g 26
6.8%
N 21
 
5.5%
i 21
 
5.5%
a 15
 
3.9%
G 15
 
3.9%
Other values (9) 57
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 60
15.6%
c 60
15.6%
n 45
11.7%
M 34
8.9%
Z 30
7.8%
g 26
6.8%
N 21
 
5.5%
i 21
 
5.5%
a 15
 
3.9%
G 15
 
3.9%
Other values (9) 57
14.8%

Me2
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
In
33 
Ti
30 
Fe
23 
Sc
19 
W
18 
Other values (8)
69 

Length

Max length2
Median length2
Mean length1.90625
Min length1

Characters and Unicode

Total characters366
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSc
2nd rowSc
3rd rowSc
4th rowSc
5th rowSc

Common Values

ValueCountFrequency (%)
In 33
17.2%
Ti 30
15.6%
Fe 23
12.0%
Sc 19
9.9%
W 18
9.4%
Nb 18
9.4%
Zr 11
 
5.7%
Ga 9
 
4.7%
Mn 8
 
4.2%
Mg 8
 
4.2%
Other values (3) 15
7.8%

Length

2023-12-05T20:29:06.268107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 33
17.2%
ti 30
15.6%
fe 23
12.0%
sc 19
9.9%
w 18
9.4%
nb 18
9.4%
zr 11
 
5.7%
ga 9
 
4.7%
mn 8
 
4.2%
mg 8
 
4.2%
Other values (3) 15
7.8%

Most occurring characters

ValueCountFrequency (%)
n 47
12.8%
I 33
 
9.0%
T 30
 
8.2%
i 30
 
8.2%
b 25
 
6.8%
e 23
 
6.3%
F 23
 
6.3%
S 19
 
5.2%
c 19
 
5.2%
W 18
 
4.9%
Other values (10) 99
27.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 192
52.5%
Lowercase Letter 174
47.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 33
17.2%
T 30
15.6%
F 23
12.0%
S 19
9.9%
W 18
9.4%
N 18
9.4%
Z 17
8.9%
M 16
8.3%
G 9
 
4.7%
Y 7
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
n 47
27.0%
i 30
17.2%
b 25
14.4%
e 23
13.2%
c 19
10.9%
r 11
 
6.3%
a 9
 
5.2%
g 8
 
4.6%
u 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 366
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 47
12.8%
I 33
 
9.0%
T 30
 
8.2%
i 30
 
8.2%
b 25
 
6.8%
e 23
 
6.3%
F 23
 
6.3%
S 19
 
5.2%
c 19
 
5.2%
W 18
 
4.9%
Other values (10) 99
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 47
12.8%
I 33
 
9.0%
T 30
 
8.2%
i 30
 
8.2%
b 25
 
6.8%
e 23
 
6.3%
F 23
 
6.3%
S 19
 
5.2%
c 19
 
5.2%
W 18
 
4.9%
Other values (10) 99
27.0%

Me3
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Ti
192 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters384
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTi
2nd rowTi
3rd rowTi
4th rowTi
5th rowTi

Common Values

ValueCountFrequency (%)
Ti 192
100.0%

Length

2023-12-05T20:29:06.352177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:29:06.418569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
ti 192
100.0%

Most occurring characters

ValueCountFrequency (%)
T 192
50.0%
i 192
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 192
50.0%
Lowercase Letter 192
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 192
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 192
50.0%
i 192
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 192
50.0%
i 192
50.0%

frac-Me1
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54262598
Minimum0.05
Maximum0.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:06.582363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.25
Q10.5
median0.5
Q30.67
95-th percentile0.75
Maximum0.88
Range0.83
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14095727
Coefficient of variation (CV)0.25976874
Kurtosis1.6347618
Mean0.54262598
Median Absolute Deviation (MAD)0
Skewness-0.36968089
Sum104.18419
Variance0.019868952
MonotonicityNot monotonic
2023-12-05T20:29:06.664479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.5 120
62.5%
0.75 24
 
12.5%
0.67 18
 
9.4%
0.25 6
 
3.1%
0.17 2
 
1.0%
0.545454545 1
 
0.5%
0.88 1
 
0.5%
0.47 1
 
0.5%
0.27 1
 
0.5%
0.864864865 1
 
0.5%
Other values (17) 17
 
8.9%
ValueCountFrequency (%)
0.05 1
 
0.5%
0.1 1
 
0.5%
0.125 1
 
0.5%
0.15 1
 
0.5%
0.17 2
 
1.0%
0.25 6
 
3.1%
0.27 1
 
0.5%
0.47 1
 
0.5%
0.5 120
62.5%
0.545454545 1
 
0.5%
ValueCountFrequency (%)
0.88 1
 
0.5%
0.864864865 1
 
0.5%
0.857142857 1
 
0.5%
0.839640795 1
 
0.5%
0.817184644 1
 
0.5%
0.81 1
 
0.5%
0.75 24
12.5%
0.69 1
 
0.5%
0.679281591 1
 
0.5%
0.67 18
9.4%

frac-Me2
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43914485
Minimum0.12
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:06.747375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.25
Q10.33
median0.5
Q30.5
95-th percentile0.75
Maximum0.95
Range0.83
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1501794
Coefficient of variation (CV)0.34198147
Kurtosis1.0067012
Mean0.43914485
Median Absolute Deviation (MAD)0
Skewness0.47876006
Sum84.315811
Variance0.022553852
MonotonicityNot monotonic
2023-12-05T20:29:06.828795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.5 106
55.2%
0.25 38
 
19.8%
0.33 18
 
9.4%
0.75 6
 
3.1%
0.83 2
 
1.0%
0.454545455 1
 
0.5%
0.12 1
 
0.5%
0.53 1
 
0.5%
0.73 1
 
0.5%
0.135135135 1
 
0.5%
Other values (17) 17
 
8.9%
ValueCountFrequency (%)
0.12 1
 
0.5%
0.135135135 1
 
0.5%
0.142857143 1
 
0.5%
0.160359205 1
 
0.5%
0.182815356 1
 
0.5%
0.19 1
 
0.5%
0.25 38
19.8%
0.31 1
 
0.5%
0.320718409 1
 
0.5%
0.33 18
9.4%
ValueCountFrequency (%)
0.95 1
 
0.5%
0.9 1
 
0.5%
0.875 1
 
0.5%
0.85 1
 
0.5%
0.83 2
 
1.0%
0.75 6
 
3.1%
0.73 1
 
0.5%
0.53 1
 
0.5%
0.5 106
55.2%
0.454545455 1
 
0.5%

frac-Me3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0.0
178 
0.25
 
14

Length

Max length4
Median length3
Mean length3.0729167
Min length3

Characters and Unicode

Total characters590
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 178
92.7%
0.25 14
 
7.3%

Length

2023-12-05T20:29:06.916775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:29:06.989739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 178
92.7%
0.25 14
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 370
62.7%
. 192
32.5%
2 14
 
2.4%
5 14
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 398
67.5%
Other Punctuation 192
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 370
93.0%
2 14
 
3.5%
5 14
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 370
62.7%
. 192
32.5%
2 14
 
2.4%
5 14
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 370
62.7%
. 192
32.5%
2 14
 
2.4%
5 14
 
2.4%

PT-content
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58775521
Minimum0
Maximum1
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:07.073454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.46
median0.6
Q30.72125
95-th percentile0.9
Maximum1
Range1
Interquartile range (IQR)0.26125

Descriptive statistics

Standard deviation0.2104026
Coefficient of variation (CV)0.35797658
Kurtosis-0.16504904
Mean0.58775521
Median Absolute Deviation (MAD)0.135
Skewness-0.37400405
Sum112.849
Variance0.044269254
MonotonicityNot monotonic
2023-12-05T20:29:07.178462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.5 20
 
10.4%
0.6 18
 
9.4%
0.7 18
 
9.4%
0.8 17
 
8.9%
0.9 15
 
7.8%
0.4 11
 
5.7%
0.65 8
 
4.2%
0.3 7
 
3.6%
0.2 6
 
3.1%
0.85 5
 
2.6%
Other values (36) 67
34.9%
ValueCountFrequency (%)
0 2
 
1.0%
0.1 4
2.1%
0.2 6
3.1%
0.25 1
 
0.5%
0.27 1
 
0.5%
0.28 1
 
0.5%
0.29 1
 
0.5%
0.3 7
3.6%
0.31 1
 
0.5%
0.35 2
 
1.0%
ValueCountFrequency (%)
1 2
 
1.0%
0.95 3
 
1.6%
0.9 15
7.8%
0.85 5
 
2.6%
0.8 17
8.9%
0.78 1
 
0.5%
0.75 4
 
2.1%
0.7265 1
 
0.5%
0.7195 1
 
0.5%
0.7 18
9.4%

Tolerance_Factor
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98769323
Minimum0.9223
Maximum1.0272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:07.279943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.9223
5-th percentile0.955695
Q10.9773
median0.98565
Q31.00265
95-th percentile1.018845
Maximum1.0272
Range0.1049
Interquartile range (IQR)0.02535

Descriptive statistics

Standard deviation0.019938657
Coefficient of variation (CV)0.020187095
Kurtosis0.40500717
Mean0.98769323
Median Absolute Deviation (MAD)0.01195
Skewness-0.39185681
Sum189.6371
Variance0.00039755006
MonotonicityNot monotonic
2023-12-05T20:29:07.389659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9781 4
 
2.1%
0.9773 4
 
2.1%
0.9851 3
 
1.6%
0.9777 3
 
1.6%
0.9854 3
 
1.6%
0.9808 3
 
1.6%
1.0202 3
 
1.6%
0.9785 3
 
1.6%
1.0022 3
 
1.6%
0.9841 2
 
1.0%
Other values (136) 161
83.9%
ValueCountFrequency (%)
0.9223 1
0.5%
0.9229 1
0.5%
0.9345 1
0.5%
0.9389 1
0.5%
0.944 1
0.5%
0.9461 1
0.5%
0.9487 2
1.0%
0.9523 1
0.5%
0.9541 1
0.5%
0.957 1
0.5%
ValueCountFrequency (%)
1.0272 2
1.0%
1.0236 1
 
0.5%
1.0214 1
 
0.5%
1.0202 3
1.6%
1.0201 1
 
0.5%
1.0189 2
1.0%
1.0188 1
 
0.5%
1.0179 1
 
0.5%
1.0174 1
 
0.5%
1.0173 1
 
0.5%

Valence_Electron_Number
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9090104
Minimum7.995
Maximum14.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:07.493250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7.995
5-th percentile8
Q18
median8
Q39.5
95-th percentile11.6225
Maximum14.75
Range6.755
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.3471921
Coefficient of variation (CV)0.15121681
Kurtosis2.3444896
Mean8.9090104
Median Absolute Deviation (MAD)0
Skewness1.6177349
Sum1710.53
Variance1.8149266
MonotonicityNot monotonic
2023-12-05T20:29:07.597630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 100
52.1%
11 5
 
2.6%
9 5
 
2.6%
10 5
 
2.6%
8.5 4
 
2.1%
9.5 4
 
2.1%
12.5 3
 
1.6%
10.5 3
 
1.6%
9.6 2
 
1.0%
8.75 2
 
1.0%
Other values (48) 59
30.7%
ValueCountFrequency (%)
7.995 2
 
1.0%
7.996 1
 
0.5%
7.997 1
 
0.5%
7.998 1
 
0.5%
7.999 1
 
0.5%
8 100
52.1%
8.2 1
 
0.5%
8.25 1
 
0.5%
8.38 1
 
0.5%
8.4 2
 
1.0%
ValueCountFrequency (%)
14.75 1
 
0.5%
14 1
 
0.5%
12.5 3
1.6%
12 2
1.0%
11.75 1
 
0.5%
11.689 1
 
0.5%
11.65 1
 
0.5%
11.6 1
 
0.5%
11.55 1
 
0.5%
11.5 2
1.0%

Martynov_Batsanov_Electronegativity
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.764737
Minimum3.51
Maximum4.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:07.698820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.51
5-th percentile3.659775
Q13.73675
median3.754
Q33.788625
95-th percentile3.877325
Maximum4.1
Range0.59
Interquartile range (IQR)0.051875

Descriptive statistics

Standard deviation0.069449132
Coefficient of variation (CV)0.018447273
Kurtosis5.8937495
Mean3.764737
Median Absolute Deviation (MAD)0.0255
Skewness1.0523758
Sum722.8295
Variance0.0048231819
MonotonicityNot monotonic
2023-12-05T20:29:07.803746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.753 3
 
1.6%
3.738 3
 
1.6%
3.786 3
 
1.6%
3.7395 2
 
1.0%
3.789 2
 
1.0%
3.7338 2
 
1.0%
3.78 2
 
1.0%
3.752 2
 
1.0%
3.766 2
 
1.0%
3.783 2
 
1.0%
Other values (157) 169
88.0%
ValueCountFrequency (%)
3.51 1
0.5%
3.564 1
0.5%
3.612 1
0.5%
3.618 1
0.5%
3.633 1
0.5%
3.6435 1
0.5%
3.645 1
0.5%
3.648 1
0.5%
3.654 2
1.0%
3.6645 1
0.5%
ValueCountFrequency (%)
4.1 1
0.5%
4.06 1
0.5%
4.02 1
0.5%
3.98 1
0.5%
3.94 1
0.5%
3.9075 1
0.5%
3.9 1
0.5%
3.899 1
0.5%
3.8905 1
0.5%
3.882 1
0.5%

Ideal_Bond_Length_Sum
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9106776
Minimum3.8462
Maximum3.9875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:07.905771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.8462
5-th percentile3.8636
Q13.8927
median3.91325
Q33.93105
95-th percentile3.94888
Maximum3.9875
Range0.1413
Interquartile range (IQR)0.03835

Descriptive statistics

Standard deviation0.027077807
Coefficient of variation (CV)0.0069240703
Kurtosis-0.34494355
Mean3.9106776
Median Absolute Deviation (MAD)0.02
Skewness-0.25123609
Sum750.8501
Variance0.00073320761
MonotonicityNot monotonic
2023-12-05T20:29:08.011545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9106 3
 
1.6%
3.8967 3
 
1.6%
3.9398 2
 
1.0%
3.9402 2
 
1.0%
3.9512 2
 
1.0%
3.9286 2
 
1.0%
3.9195 2
 
1.0%
3.927 2
 
1.0%
3.893 2
 
1.0%
3.9088 2
 
1.0%
Other values (164) 170
88.5%
ValueCountFrequency (%)
3.8462 1
0.5%
3.848 1
0.5%
3.8481 1
0.5%
3.8532 1
0.5%
3.8563 1
0.5%
3.8597 1
0.5%
3.8606 1
0.5%
3.8609 1
0.5%
3.861 1
0.5%
3.8614 1
0.5%
ValueCountFrequency (%)
3.9875 1
0.5%
3.9754 1
0.5%
3.9633 1
0.5%
3.9612 1
0.5%
3.9512 2
1.0%
3.9504 1
0.5%
3.95 1
0.5%
3.9495 1
0.5%
3.9491 1
0.5%
3.9487 1
0.5%

Mendeleev_Number
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.71304
Minimum99.7
Maximum147.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-05T20:29:08.118839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum99.7
5-th percentile116.64768
Q1122.155
median127.55
Q3133.3115
95-th percentile139.3675
Maximum147.85
Range48.15
Interquartile range (IQR)11.1565

Descriptive statistics

Standard deviation7.6742182
Coefficient of variation (CV)0.060089544
Kurtosis0.96087181
Mean127.71304
Median Absolute Deviation (MAD)5.6043
Skewness-0.38005326
Sum24520.903
Variance58.893624
MonotonicityNot monotonic
2023-12-05T20:29:08.227638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.6 3
 
1.6%
131.2 3
 
1.6%
121.75 3
 
1.6%
128.2 2
 
1.0%
132.4 2
 
1.0%
126.8 2
 
1.0%
129.6 2
 
1.0%
130.3 2
 
1.0%
131 2
 
1.0%
121.3 2
 
1.0%
Other values (149) 169
88.0%
ValueCountFrequency (%)
99.7 1
0.5%
102.4 1
0.5%
105.1 1
0.5%
107.8 1
0.5%
110.5 1
0.5%
113.2 1
0.5%
114.937 1
0.5%
115.9 1
0.5%
116.134 1
0.5%
116.544 1
0.5%
ValueCountFrequency (%)
147.85 1
0.5%
145.2 1
0.5%
144.6 1
0.5%
142.025 1
0.5%
142 1
0.5%
141.5 1
0.5%
140.7375 1
0.5%
140.2 1
0.5%
139.9 1
0.5%
139.45 1
0.5%

Formability
Boolean

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
True
125 
False
67 
ValueCountFrequency (%)
True 125
65.1%
False 67
34.9%
2023-12-05T20:29:08.310132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Interactions

2023-12-05T20:29:05.013703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.467953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.021268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.541206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.065691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.612166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.259118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.839618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.420125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.072625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.528352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.078422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.598546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.123453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.673808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.321731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.902274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.485673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.130462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.584693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.129907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.649864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.179469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.728621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.380259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.962455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.544736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.185861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.638246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.179440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.700609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.232903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.875778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.440733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.019848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.603068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.245712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.698851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.237198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.758397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.290623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.936579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.502568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.084412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.667527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.308856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.761630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.295771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.817385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.352659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.996658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.567492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.150416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.736934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.373242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.827666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.357549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.880148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.417734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.062364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.634365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.219172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.808577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.442547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.891328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.419869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.942657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.482812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.127921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.704383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.285755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.878496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:05.511254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:00.956851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:01.484378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.006563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:02.550369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.196557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:03.775607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.354815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:29:04.948079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T20:29:08.368671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CompoundFormabilityIdeal_Bond_Length_SumMartynov_Batsanov_ElectronegativityMe1Me2Mendeleev_NumberPT-contentTolerance_FactorValence_Electron_Numberfrac-Me1frac-Me2frac-Me3x(BiMe1Me2)O3
Compound1.0000.4760.482-0.2040.9470.947-0.2260.099-0.136-0.1160.145-0.1000.940-0.099
Formability0.4761.0000.1350.2650.4080.324-0.3150.3780.427-0.0300.032-0.0470.000-0.378
Ideal_Bond_Length_Sum0.4820.1351.000-0.4180.3740.457-0.7100.4920.113-0.568-0.0140.0130.364-0.492
Martynov_Batsanov_Electronegativity-0.2040.265-0.4181.0000.4260.3820.3090.1870.4840.571-0.2130.2970.041-0.187
Me10.9470.4080.3740.4261.0000.710-0.1920.017-0.1410.1790.276-0.3260.348-0.017
Me20.9470.3240.4570.3820.7101.0000.1400.022-0.0150.0510.026-0.0810.971-0.022
Mendeleev_Number-0.226-0.315-0.7100.309-0.1920.1401.000-0.369-0.1450.4170.1110.0490.5330.369
PT-content0.0990.3780.4920.1870.0170.022-0.3691.0000.890-0.2720.072-0.0330.102-1.000
Tolerance_Factor-0.1360.4270.1130.484-0.141-0.015-0.1450.8901.000-0.051-0.0190.0620.000-0.890
Valence_Electron_Number-0.116-0.030-0.5680.5710.1790.0510.417-0.272-0.0511.000-0.0310.0620.0000.272
frac-Me10.1450.032-0.014-0.2130.2760.0260.1110.072-0.019-0.0311.000-0.8580.092-0.072
frac-Me2-0.100-0.0470.0130.297-0.326-0.0810.049-0.0330.0620.062-0.8581.0000.5330.033
frac-Me30.9400.0000.3640.0410.3480.9710.5330.1020.0000.0000.0920.5331.0000.057
x(BiMe1Me2)O3-0.099-0.378-0.492-0.187-0.017-0.0220.369-1.000-0.8900.272-0.0720.0330.0571.000

Missing values

2023-12-05T20:29:05.606931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T20:29:05.764877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Compoundx(BiMe1Me2)O3Me1Me2Me3frac-Me1frac-Me2frac-Me3PT-contentTolerance_FactorValence_Electron_NumberMartynov_Batsanov_ElectronegativityIdeal_Bond_Length_SumMendeleev_NumberFormability
0Bi(GaSc)0.45GaScTi0.250.750.00.550.98138.03.73953.9208118.9375No
1Bi(GaSc)0.40GaScTi0.250.750.00.600.98648.03.74403.9215119.5000No
2Bi(GaSc)0.38GaScTi0.250.750.00.620.98848.03.74583.9218119.7250No
3Bi(GaSc)0.36GaScTi0.250.750.00.640.99048.03.74763.9221119.9500No
4Bi(GaSc)0.30GaScTi0.250.750.00.700.99668.03.75303.9229120.6250Yes
5Bi(GaSc)0.20GaScTi0.250.750.00.801.00688.03.76203.9243121.7500Yes
6Bi(MgW)0.80MgWTi0.750.250.00.200.95418.03.61203.8606144.6000No
7Bi(MgW)0.70MgWTi0.750.250.00.300.96338.03.63303.8689142.0250No
8Bi(MgW)0.65MgWTi0.750.250.00.350.96788.03.64353.8731140.7375No
9Bi(MgW)0.60MgWTi0.750.250.00.400.97248.03.65403.8772139.4500No
Compoundx(BiMe1Me2)O3Me1Me2Me3frac-Me1frac-Me2frac-Me3PT-contentTolerance_FactorValence_Electron_NumberMartynov_Batsanov_ElectronegativityIdeal_Bond_Length_SumMendeleev_NumberFormability
182BiScIn0.3700ScInTi0.5945950.4054050.00.63000.98088.0003.74773.9409123.6100Yes
183BiScIn0.3118ScInTi0.6792820.3207180.00.68820.98878.0003.74933.9373121.9814Yes
184BiScIn0.2735ScInTi0.8171850.1828150.00.72650.99438.0003.74823.9340119.8155Yes
185BiScIn0.3118ScInTi0.8396410.1603590.00.68820.98998.0003.74283.9346118.7814Yes
186BiScIn0.3500ScInTi0.8571430.1428570.00.65000.98548.0003.73753.9353117.7500Yes
187BiScIn0.3700ScInTi0.8648650.1351350.00.63000.98318.0003.73473.9356117.2100Yes
188BiCoFe0.7000CoFeTi0.2700000.7300000.00.30000.981211.6893.81053.8481136.4670No
189Bi(GaSc)0.2500ScGaTi0.4700000.5300000.00.75001.00578.0003.77153.9152125.5975Yes
190BiCoFe0.2000CoFeTi0.8800000.1200000.00.80001.01629.1763.79483.8905127.9280Yes
191Bi(ScFe)0.4000ScFeTi0.8100000.1900000.00.60000.98428.3803.73693.9260116.5440Yes

Duplicate rows

Most frequently occurring

Compoundx(BiMe1Me2)O3Me1Me2Me3frac-Me1frac-Me2frac-Me3PT-contentTolerance_FactorValence_Electron_NumberMartynov_Batsanov_ElectronegativityIdeal_Bond_Length_SumMendeleev_NumberFormability# duplicates
0Bi(MgNb)0.5MgNbTi0.670.330.00.50.98077.9953.73383.8930135.535No2
1Bi(ZnNb)0.5ZnNbTi0.670.330.00.50.977711.3453.77743.8967135.870No2